Computer-Generated Emotional Face Retrieval with P300 Signals of Multiple Subjects

Abstract

Applying brain signals to human-computer interaction enables us to detect the attention. Based on P300 signals – one type of event-related potential – enables brain-machine interface users to select desired letters by means of attention alone. Previous studies have reported the feasibility of P300 signals in enabling a single subject to realize novel information retrieval. In the recent collaborative EEG study of multiple subjects has enabled classification to detect attention in a markedly improved way. Here we propose emotional face retrieval using P300 signals of 20 subjects. As a result, the F-measure under the condition of a single subject was a standard deviation of 0.636 ± 0.05 and an F-measure of 0.886 with multiple subjects. In short, emotional face retrieval classification is improved with collaborative P300 signals from multiple subjects. This technique could be applied to life logs, computer-supported cooperative work, and neuromarketing.